Name
Abstract | ||
|---|---|---|
In this paper a novel consistency-based Gaussian mixture nonlinear filter (CbGMF) is proposed where the distribution of the target state is represented by a dynamic set (mixture) of Gaussian distributions (“subtracks”). The subtracks are generated using a consistency-based filtering rule for the EKF and a novel approach for consistent track splitting. Simulation results show that the CbGMF has performance superior to previous algorithms for a tracking problem with a contact lens shaped uncertainty in the state estimation error as well as in keeping the range estimation error small in the early stages of the filtering. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/TAES.2014.120749 | IEEE Trans. Aerospace and Electronic Systems |
Keywords | Field | DocType |
Uncertainty,Gaussian processes,Target tracking,Measurement uncertainty,Loss measurement,Radar tracking | Mathematical optimization,Extended Kalman filter,Gaussian random field,Control theory,Contact lens,Filter (signal processing),Algorithm,Gaussian,Nonlinear filter,Gaussian function,Mathematics | Journal |
Volume | Issue | ISSN |
50 | 3 | 0018-9251 |
Citations | PageRank | References |
2 | 0.43 | 5 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xin Tian | 1 | 43 | 9.35 |
| Yaakov Bar-Shalom | 2 | 460 | 99.56 |